1. Field of the Invention
An aspect of the present invention relates to at least one of a spectral characteristic acquisition device, an image evaluation device, and an image formation apparatus.
2. Description of the Related Art
In an image formation apparatus such as a printing apparatus or a printer, one of important technical problems is a control of color tone such as color stability or color reproducibility. In recent years, an image formation apparatus has been realized that installs a spectrometer such as a spectrophotometer for a control of color tone.
In such an image formation apparatus, a colorimetric value such as XYZ or L*a*b* defined in CIE (International Commission on Illumination) is obtained from a spectral characteristic of diffused or reflected light from a print surface as measured by a spectrometer to execute check of color tone of a print or adjustment of an image formation process.
For example, a spectrometer for measuring visible light detects light in a wavelength range of 400-700 nm for each wavelength band with a pitch of 10 nm and outputs 31 or more digitized values. A measurement of a spectral characteristic needs a certain period of time because diffused or reflected from a surface to be measured is temporally and spatially divided into 31 or more to acquire a light intensity signal. Therefore, for example, in a case where an in-line measurement is executed for an output image at a rate corresponding to a printing speed thereof in an image formation apparatus for executing high-speed printing, a rate of detection may be insufficient so that application thereof may be difficult.
Then, in a case where a spectral characteristic of a measurement object with a comparatively smoothly changed spectral characteristic distribution, such as a printed image, is measured, for example, a method has been known that detects light in a comparatively few or about 3-16 wavelength bands referred to as a multiband by a spectrometer and estimates a spectral characteristic of a measurement object from a detection result thereof (for example, see Norimichi Tsumura, Hideaki Haneishi, Youichi Miyake, “Estimation of Spectral Reflectances from Multi-Band Images by Multiple Regression Analysis”, Japanese Journal of Optics, Vol. 27, No. 7, pp. 384-391 (1998)).
According to such a method, it is possible to reduce a period of time necessary for detection because the number of wavelength bands to be detected is small, and it is also possible to be applied to a field required for a high-speed measurement such as an in-line measurement for a printed image. Furthermore, for example, it is possible to estimate, at high precision, a spectral characteristic of a measurement object provided in such a manner that it is possible to acquire statistical information with respect to a spectral characteristic thereof preliminarily, such as a printed image with a color reproduced by a combination of about 4 kinds of color materials.
For example, estimation of a spectral characteristic is executed by using a transformation matrix that is obtained from a measurement result for a standard sample with a known spectral characteristic. It is preferable for a transformation matrix to be obtained from a standard sample that has a feature approximating a spectral characteristic of a measurement object, in order to estimate such a spectral characteristic at high precision. Furthermore, it is preferable to set a plurality of transformation matrices dependent on a feature of a measurement object or the like in such a manner that it is possible to handle a variety of measurement objects. However, it is necessary to prepare and measure an enormous number of standard samples and execute an operation process thereof in order to obtain a plurality of transformation matrices, so that a lot of labor and coat is required.
Then, a method has been disclosed that produces, by calculation, a large amount of learning data to be used for calculation of a transformation matrix and obtains a transformation matrix based on the learning data obtained by the calculation instead of practically measuring a standard sample (for example, see Japanese Patent Application Publication No. 2012-154711).
However, in the method as described above, there is a possibility that an estimation error or a calculation error is included in learning data produced by calculation and these errors also influence a transformation matrix to be obtained based on the learning data so that precision of estimation of a spectral characteristic is lowered.